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Papers
61,005 resultsShowing papers similar to A portable AI-powered rotifer-tracking system for in-situ water quality assessment
ClearDevelopment of an Iot-Integrated AI System for Microplastic Detection in Water Samples
Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines five analytical techniques to detect contaminants including heavy metals, pathogens, and microplastics in real time. The device uses an embedded machine learning model trained on diverse water samples to recognize contamination patterns. The study demonstrates a cost-effective approach to rapid water quality monitoring that could help identify microplastic pollution in both industrial and domestic water supplies.
Artificial intelligence (AI) based rapid water testing system
Researchers developed an AI-powered portable water testing system that integrates five analytical techniques for real-time water quality monitoring. The system can detect a range of contaminants including microplastics, heavy metals, and pathogens within seconds, offering a cost-effective alternative to traditional laboratory-based water testing for both industrial and domestic use.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines multiple sensing techniques, including capacitance, resistance, UV, infrared, and Raman spectroscopy, to detect contaminants in real time. The system can identify a wide range of pollutants including microplastics, heavy metals, and organic compounds within seconds. The device aims to provide an accessible, rapid monitoring tool for water quality assessment in both industrial and domestic settings.
A Novel Low-Cost Approach For Detection, Classification, and Quantification of Microplastic Pollution in Freshwater Ecosystems using IoT devices and Instance Segmentation
Researchers developed a novel low-cost IoT-based system combining instance segmentation algorithms for the automated detection, classification, and quantification of microplastic pollution in freshwater ecosystems, addressing the scalability limitations of conventional laboratory methods. The approach demonstrated feasibility for wide-scale environmental monitoring by enabling real-time microplastic analysis without expensive laboratory infrastructure.
zero-plastic: AI-assisted Sensing for Microplastic Assessment
Researchers developed the 'zero-plastic' open-source imaging system combining flow microscopy with AI classification for low-cost, real-time microplastic monitoring in water, and integrated it with a digital twin infrastructure for distributed environmental sensing.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
A field deployable imaging system for detecting microplastics in the aquatic environment
Researchers built a portable imaging system for detecting microplastics in water that can be deployed directly in the field rather than requiring laboratory analysis. The system uses a de-scattering algorithm to produce clear images even in turbid water conditions and can identify particles as small as 50 micrometers. This low-cost tool could make routine microplastic monitoring of rivers, lakes, and coastal waters much more practical and accessible.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.
Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi
Researchers applied deep learning to analyze light scattering patterns from mixed particles in ocean water, enabling automated identification of different particle types including sediment and biological material. This technology could be adapted to detect and classify microplastics in marine environments alongside natural particles.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
Portable On-Site Optical Detection and Quantification of Microplastics
Researchers built a portable, on-site optical device to detect and quantify microplastics in water. The device addresses the challenge of detecting small, often translucent particles without a laboratory setting. Portable microplastic detection tools could enable real-time monitoring in the field, supporting faster environmental assessments.
Microplastic identification in marine environments: A low-cost and effective approach based on transmitted light measurements
Researchers designed a low-cost microplastic detection system using a standard LCD panel and a digital USB microscope to measure transmitted light through seawater samples. The compact system demonstrated effective detection and quantification of microplastics without the need for expensive laboratory instrumentation.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
An IoT Based Low-Cost Optical System for Early Detection of Microplastics in Water Sources
Researchers developed a low-cost device that can detect tiny plastic particles (microplastics) in drinking water using simple LED lights and sensors, which could make testing much cheaper and easier than current lab methods. This matters because microplastics are found in water supplies worldwide and may pose health risks, but expensive testing equipment has made it hard to monitor water quality regularly. The study shows this simpler technology could work, potentially helping communities better track plastic pollution in their water sources.
IoT-Integrated Image Recognition System for Microplastic Detection and Classification
Researchers developed an IoT-based system that combines a microscopic camera with a YOLOv8 deep learning model to detect and classify microplastics in real time, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy across diverse environmental conditions and visualizes data through a cloud-based dashboard. This scalable approach offers a practical tool for monitoring microplastic pollution, with potential for future integration on marine vessels.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Holographic imaging and machine learning for microplastic size and shape analysis in water
Researchers used a portable holographic camera paired with deep-learning AI to rapidly measure the size and shape of microplastics floating in water, finding the lightweight MobileNetV2 model outperformed the larger ResNet101 in classification accuracy. The method offers a cost-effective, field-deployable tool for monitoring microplastics in drinking water at scale.
Economical and Novel Microplastic Detection Using a Arduino-Based Turbidity Sensor: A Comprehensive Investigation
Researchers developed a low-cost Arduino-based turbidity sensor system for microplastic detection as an accessible alternative to expensive FTIR and Raman spectroscopy methods. The sensor demonstrated the ability to detect microplastic-induced changes in water clarity, offering a practical monitoring tool for low-resource settings and smaller waterways that are typically undersampled.
An IoT Based Low-Cost Optical System for Early Detection of Microplastics in Water Sources
Scientists have developed a low-cost system that can detect tiny plastic particles (microplastics) in drinking water using simple light sensors and internet technology. This matters because microplastics are found in tap water worldwide and may pose health risks when we drink them, but current detection methods are too expensive for regular monitoring. The new system could make it easier and cheaper to check water quality continuously, helping protect people from plastic pollution in their drinking water.
AI-Prepared Autonomous Freshwater Monitoring and Sea Ground Detection by an Autonomous Surface Vehicle
Researchers developed an AI-guided autonomous surface vehicle capable of monitoring freshwater quality, mapping lake bathymetry, and detecting underwater objects, offering a new tool for intensive climate-change-driven water body surveillance.
Identification of Microplastics in Aquatic Environments Using Oxidative Treatment and Automated Image Analysis
Researchers developed a cost-effective and replicable method for detecting microplastics in freshwater environments using oxidative treatment to digest organic matter from water samples, enabling cleaner isolation and more accurate identification of MP particles without requiring expensive instrumentation.